SSANet—Novel Residual Network for Computer‐Aided Diagnosis of Pulmonary Nodules in Chest Computed Tomography

ABSTRACT The manifestations of early lung cancer in medical imaging often appear as pulmonary nodules, which can be classified as benign or malignant. In recent years, there has been a gradual application of deep learning‐based computer‐aided diagnosis technology to assist in the diagnosis of lung nodules. This study introduces a novel three‐dimensional (3D) residual network called SSANet, which integrates split‐based convolution, shuffle attention, and a novel activation function. The aim is to enhance the accuracy of distinguishing between benign and malignant lung nodules using convolutional neural networks (CNNs) and alleviate the burden on doctors when interpreting the images. To fully extract pulmonary nodule information from chest CT images, the original residual network is expanded into a 3D CNN structure. Additionally, a 3D split‐based convolutional operation (SPConv) is designed and integrated into the feature extraction module to reduce redundancy in feature maps and improve network inference speed. In the SSABlock part of the proposed network, ACON (Activated or Not) function is also introduced. The proposed SSANet also incorporates an attention module to capture critical characteristics of lung nodules. During the training process, the PolyLoss function is utilized. Once SSANet generates the diagnosis result, a heatmap displays using Score‐CAM is employed to evaluate whether the network accurately identifies the location of lung nodules. In the final test set, the proposed network achieves an accuracy of 89.13%, an F1‐score of 84.85%, and a G‐mean of 86.20%. These metrics represent improvements of 5.43%, 5.98%, and 4.09%, respectively, compared with the original base network. The experimental results align with those of previous studies on pulmonary nodule diagnosis networks, confirming the reliability and clinical applicability of the diagnostic outcomes.